#British patient cohort (2020_02_03)
#CD29_Aragorn_Organoids_D90Std_123 - AHO17-3
#CD29_Aragorn_Organoids_D90MGd21_123 AHO17-3
#CD32_Aragorn_Organoids_123 AHO17-3
#CD32_Boromir_Organoids_256 AT1 (c5)
#CD32_Frodo_Organoids_146 AT210 (c2)
#CD32_Gandalf_Organoids_123 NI (c8)
#CD32_Gimli_Organoids_123 AT210 (c3)
#CD32_Merry_Organoids_136 AT1 (c4)

#CD29_Aragorn_D90_Std_1 BC21    AGCAGTTA
#CD29_Aragorn_D90_Std_2 BC22    CTTGTACC
#CD29_Aragorn_D90_Std_3 BC23    GAACCCGG
#CD29_Aragorn_D90_MGd21_1   BC24    TCGTAGAT
#CD29_Aragorn_D90_MGd21_2   BC25    ACGCGGAA
#CD29_Aragorn_D90_MGd21_3   BC26    CGCTATCC
#CD32_Aragorn_D60_1 BC27    GTTGCATG
#CD32_Aragorn_D60_2 BC28    TAAATCGT
#CD32_Aragorn_D60_3 BC29    ATCGCCAT
#CD32_Boromir_D60_2 BC30    CATAAAGG
#CD32_Boromir_D60_5 BC31    TCACGGTA
#CD32_Boromir_D60_6 BC32    CACTCAAC
#CD32_Frodo_D60_1   BC29    ATCGCCAT
#CD32_Frodo_D60_4   BC30    CATAAAGG
#CD32_Frodo_D60_6   BC31    TCACGGTA
#CD32_Gandalf_D60_1 BC32    CACTCAAC
#CD32_Gandalf_D60_2 BC33    GCTGTGTA
#CD32_Gandalf_D60_3 BC34    TTGCGTCG
#CD32_Gimli_D60_1   BC35    ATATGAGA
#CD32_Gimli_D60_2   BC36    CACCTCAG
#CD32_Gimli_D60_3   BC37    GCTACTTC
#CD32_Merry_D60_1   BC38    TGGGAGCT
#CD32_Merry_D60_3   BC39    ATCCGGCA
#CD32_Merry_D60_6   BC40    CCGTTATG

#Aussie cohort A (2020_03_30)
#CD31 Gimli D60 125 AT210 (c3)
#CD31 Legolas D60 146 NI (c3)
#CD33 Bofur D60 123 - AT34
#CD33 Bombur D60 123 - AT34 (1.2allelictarg)
#CD33 Dwalin D60 123 - AT32 (2o1b) (CORR)
#CD33 Gloin D60 123 AT34 (corr 2oc3) (CORR)
#CD33 Kili D60 123 AT32 (20c1 corr)
#CD33 Oin D60 123 C32
#CD34 Dori D60 123 AT32 (c16)
#CD33 Ori D60 123 C11

#CD31 Gimli D60 125_1   BC21    AGCAGTTA    Pool3
#CD31 Gimli D60 125_2   BC22    CTTGTACC    Pool3
#CD31 Gimli D60 125_3   BC23    GAACCCGG    Pool3
#CD31 Legolas D60 146_1 BC24    TCGTAGAT    Pool3
#CD31 Legolas D60 146_2 BC25    ACGCGGAA    Pool3
#CD31 Legolas D60 146_3 BC26    CGCTATCC    Pool3
#CD33 Bofur D60 123_1   BC27    GTTGCATG    Pool3
#CD33 Bofur D60 123_2   BC28    TAAATCGT    Pool3
#CD33 Bofur D60 123_3   BC29    ATCGCCAT    Pool3
#CD33 Bombur D60 123_1  BC30    CATAAAGG    Pool3
#CD33 Bombur D60 123_2  BC31    TCACGGTA    Pool3
#CD33 Bombur D60 123_3  BC32    CACTCAAC    Pool3
#CD33 Dwalin D60 123_1  BC33    GCTGTGTA    Pool3
#CD33 Dwalin D60 123_2  BC34    TTGCGTCG    Pool3
#CD33 Dwalin D60 123_3  BC35    ATATGAGA    Pool3
#CD33 Gloin D60 123_1   BC26    CGCTATCC    Pool4
#CD33 Gloin D60 123_2   BC27    GTTGCATG    Pool4
#CD33 Gloin D60 123_3   BC28    TAAATCGT    Pool4
#CD33 Kili D60 123_1    BC29    ATCGCCAT    Pool4
#CD33 Kili D60 123_2    BC30    CATAAAGG    Pool4
#CD33 Kili D60 123_3    BC31    TCACGGTA    Pool4
#CD33 Oin D60 123_1 BC32    CACTCAAC    Pool4
#CD33 Oin D60 123_2 BC33    GCTGTGTA    Pool4
#CD33 Oin D60 123_3 BC34    TTGCGTCG    Pool4
#CD34 Dori D60 123_1    BC35    ATATGAGA    Pool4
#CD34 Dori D60 123_2    BC36    CACCTCAG    Pool4
#CD34 Dori D60 123_3    BC37    GCTACTTC    Pool4
#CD33 Ori D60 123_1 BC38    TGGGAGCT    Pool4
#CD33 Ori D60 123_2 BC39    ATCCGGCA    Pool4
#CD33 Ori D60 123_3 BC40    CCGTTATG    Pool4

#Aussie cohort B (2020_04_22)
#CD36_Bifur_D60_123 AT34 (18.2 targmut)
#CD36_Bofur_D60_123 (AT34)
#CD36_Bombur_D60_123 AT34 (homo allelic targ 1.2)
#CD36_Dori_D60_123 AT32 (c16)
#CD36_Dwalin_D60_123 AT32 (2c1b corr)
#CD36_Fili_D60_123 AT32 (c16)
#CD36_Gloin_D60_123 AT34 (corr2oc3)
#CD36_Kili_D60_123 AT32 (2oc1 corr)
#CD36_Oin_D60_123 C32
#CD36_Ori_D60_123 C11

#CD36_Bifur_D60_123_1   BC21    AGCAGTTA    Pool5
#CD36_Bifur_D60_123_2   BC22    CTTGTACC    Pool5
#CD36_Bifur_D60_123_3   BC23    GAACCCGG    Pool5
#CD36_Bofur_D60_123_1   BC24    TCGTAGAT    Pool5
#CD36_Bofur_D60_123_2   BC25    ACGCGGAA    Pool5
#CD36_Bofur_D60_123_3   BC26    CGCTATCC    Pool5
#CD36_Bombur_D60_123_1  BC27    GTTGCATG    Pool5
#CD36_Bombur_D60_123_2  BC28    TAAATCGT    Pool5
#CD36_Bombur_D60_123_3  BC29    ATCGCCAT    Pool5
#CD36_Dori_D60_123_1    BC30    CATAAAGG    Pool5
#CD36_Dori_D60_123_2    BC31    TCACGGTA    Pool5
#CD36_Dori_D60_123_3    BC32    CACTCAAC    Pool5
#CD36_Dwalin_D60_123_1  BC33    GCTGTGTA    Pool5
#CD36_Dwalin_D60_123_2  BC34    TTGCGTCG    Pool5
#CD36_Dwalin_D60_123_3  BC35    ATATGAGA    Pool5
#CD36_Fili_D60_123_1    BC26    CGCTATCC    Pool6
#CD36_Fili_D60_123_2    BC27    GTTGCATG    Pool6
#CD36_Fili_D60_123_3    BC28    TAAATCGT    Pool6
#CD36_Gloin_D60_123_1   BC29    ATCGCCAT    Pool6
#CD36_Gloin_D60_123_2   BC30    CATAAAGG    Pool6
#CD36_Gloin_D60_123_3   BC31    TCACGGTA    Pool6
#CD36_Kili_D60_123_1    BC32    CACTCAAC    Pool6
#CD36_Kili_D60_123_2    BC33    GCTGTGTA    Pool6
#CD36_Kili_D60_123_3    BC34    TTGCGTCG    Pool6
#CD36_Oin_D60_123_1 fBC35   ATATGAGA    Pool6
#CD36_Oin_D60_123_2 BC36    CACCTCAG    Pool6
#CD36_Oin_D60_123_3 BC37    GCTACTTC    Pool6
#CD36_Ori_D60_123_1 BC38    TGGGAGCT    Pool6
#CD36_Ori_D60_123_2 BC39    ATCCGGCA    Pool6
#CD36_Ori_D60_123_3 BC40    CCGTTATG    Pool6

#2020_06_01

#Two cohorts were sequenced (British and Aus). Two batches of cohort 2 (CD33 and CD36) were sequenced, this also included resequencing of the first cohort (british, C29/CD32. CD29 is a rep of the original organoid study at D90. All other samples are at Day 60; for VISA time constraint and to capture birth of Purkinjes)

#I have transferred files from the Wellcome server to samn@cgatui.imm.ox.ac.uk /ifs/research-groups/dpag/proj001/Disease_cohort_2
#ftp://herhevto:cetlo-ekhyt-38@bsg-ftp.well.ox.ac.uk/200520_A00711_0202_BHNTKJDMXX
 
#Please also see the "additional_analyses" folder which is one level up the above #data files:
#ftp://herhevto:cetlo-ekhyt-38@bsg-ftp.well.ox.ac.uk/
#I have checked random md5s and they look good

#912c705e4a03c1950a9cd766f0f2f7b3  10X-gex-grouped/NAY8045A4.tar.gz
#912c705e4a03c1950a9cd766f0f2f7b3 

#I have extracted all the demux files and will focus initially on the RDS objects NAY8045A1 AND NAY8045A1 from '10X-aggregate.htodemux.tar.gz' using
#tar-xvf 10X-aggregate.htodemux.tar.gz
#The demux file above was superceded by the aggregate file 
#'10X-aggregate.tar.gz' as this contains preferable multiseq #demultiplexing objects. I need to also explore further #libraries


#Demultiplexing has been done using 2 different algorithms #from Seurat: MULTIseq and HTOdemux. So,

#- 10X-aggregate.tar.gz : data demultiplexed using MULTIseq,
#- 10X-aggregate.htodemux.tar.gz : data demultiplexed using HTOdemux.

#For chemical hashing, you should be using MULTIseq, but I #added the other one so you can compare the methods if not #happy.

#Regarding namings, if you take a look at the file #"samples.metadata", you will understand what each of those #names actually mean:

#- names like NAY8045A1 are Library IDs, so because one #library can be sequenced multiple times, you will see that #this value can be present in multiple rows;
#- names like 789195_GX08 are Sequencing IDs, this means a library sequenced in a specific lane/flowcell, so this value may never be present in more than one row.

#When you sequence a library more than once, you will want to group the reads together and treat them as one sample. That's when you start seeing output samples/folders with the Library ID names (e.g. NAY8045A1). If the sample was sequenced only once, the sample/folder will be named using the Sequencing ID (789195_GX08).

#- NAY8045A1 (GEX) was sequenced twice, so in the 10X-aggregate folder the output NAY8045A1 is representing the combined info from 779005_GX73 and 789196_GX73;
#- NAY8045A2 (GEX) was sequenced twice, so in the 10X-aggregate folder the output NAY8045A2 is representing the combined info from 779005_GX85 and 789196_GX85;
#- NAY8045A3, NAY8045A4, NAY8045A9, NAY8045A10 were sequenced only once, so in the 10X-aggregate folder they are represented as 789195_GX55, 789195_GX67, 789195_GX08, and 789195_GX20, respectively.

#Note, I want to be using the multiseq files, not the htodemux, though these may also be worth checking

#NAY8045A1<-readRDS("/Users/samnayler/Desktop/Disease_Seq/20200601/NAY8045A1/objects/filtered_matrix/seurat.rds")

#NAY8045A2<-readRDS("/Users/samnayler/Desktop/Disease_Seq/20200601//NAY8045A2/objects/filtered_matrix/seurat.rds")

#NAY_789195_GX08<-readRDS("/Users/samnayler/Desktop/Disease_Seq/20200601/789195_GX08/objects/filtered_matrix/seurat.rds")

#NAY_789195_GX20<-readRDS("/Users/samnayler/Desktop/Disease_Seq/20200601/789195_GX20/objects/filtered_matrix/seurat.rds")

#NAY_789195_GX55<-readRDS("/Users/samnayler/Desktop/Disease_Seq/20200601/789195_GX55/objects/filtered_matrix/seurat.rds")

#NAY_789195_GX67<-readRDS("/Users/samnayler/Desktop/Disease_Seq/20200601/789195_GX67/objects/filtered_matrix/seurat.rds")

#Note, I want to be using the multiseq files, not the htodemux, though these may also be worth checking

NAY8045A1<-readRDS("/Users/samnayler/Desktop/Disease_Seq/20200603/10X-aggregate/NAY8045A1/objects/filtered_matrix/seurat.rds")

levels(NAY8045A1@meta.data$HTO_classification)

##  [1] "BC21"      "BC21_BC22" "BC21_BC23" "BC21_BC24" "BC21_BC25" "BC21_BC26"
##  [7] "BC21_BC27" "BC21_BC28" "BC21_BC29" "BC21_BC30" "BC21_BC31" "BC21_BC32"
## [13] "BC22"      "BC22_BC23" "BC22_BC24" "BC22_BC25" "BC22_BC26" "BC22_BC27"
## [19] "BC22_BC28" "BC22_BC29" "BC22_BC30" "BC22_BC31" "BC22_BC32" "BC23"     
## [25] "BC23_BC24" "BC23_BC25" "BC23_BC26" "BC23_BC27" "BC23_BC28" "BC23_BC29"
## [31] "BC23_BC30" "BC23_BC31" "BC23_BC32" "BC24"      "BC24_BC25" "BC24_BC26"
## [37] "BC24_BC27" "BC24_BC28" "BC24_BC29" "BC24_BC30" "BC24_BC31" "BC24_BC32"
## [43] "BC25"      "BC25_BC26" "BC25_BC27" "BC25_BC28" "BC25_BC29" "BC25_BC30"
## [49] "BC25_BC31" "BC25_BC32" "BC26"      "BC26_BC27" "BC26_BC28" "BC26_BC29"
## [55] "BC26_BC30" "BC26_BC31" "BC26_BC32" "BC27"      "BC27_BC28" "BC27_BC29"
## [61] "BC27_BC30" "BC27_BC31" "BC27_BC32" "BC28"      "BC28_BC29" "BC28_BC30"
## [67] "BC28_BC31" "BC28_BC32" "BC29"      "BC29_BC30" "BC29_BC31" "BC29_BC32"
## [73] "BC30"      "BC30_BC31" "BC30_BC32" "BC31"      "BC31_BC32" "BC32"     
## [79] "Negative"

levels(NAY8045A1@meta.data$HTO_classification.global)

## [1] "Doublet"  "Negative" "Singlet"

levels(NAY8045A1@meta.data$hash.ID)

##  [1] "BC21"    "BC22"    "BC23"    "BC24"    "BC25"    "BC26"    "BC27"   
##  [8] "BC28"    "BC29"    "BC30"    "BC31"    "BC32"    "Doublet"

NAY8045A2<-readRDS("/Users/samnayler/Desktop/Disease_Seq/20200603/10X-aggregate/NAY8045A2/objects/filtered_matrix/seurat.rds")


NAY_789195_GX08<-readRDS("/Users/samnayler/Desktop/Disease_Seq/20200603/10X-aggregate/789195_GX08/objects/filtered_matrix/seurat.rds")


NAY_789195_GX20<-readRDS("/Users/samnayler/Desktop/Disease_Seq/20200603/10X-aggregate/789195_GX20/objects/filtered_matrix/seurat.rds")

NAY_789195_GX55<-readRDS("/Users/samnayler/Desktop/Disease_Seq/20200603/10X-aggregate/789195_GX55/objects/filtered_matrix/seurat.rds")

NAY_789195_GX67<-readRDS("/Users/samnayler/Desktop/Disease_Seq/20200603/10X-aggregate/789195_GX67/objects/filtered_matrix/seurat.rds")

#https://satijalab.org/seurat/v3.1/sctransform_vignette.html
#NAY8045A1<-PercentageFeatureSet(NAY8045A1, pattern ="^MT-", col.name = "percent.mt")
#NAY8045A1<-SCTransform(NAY8045A1, vars.to.regress = "percent.mt", verbose = FALSE)
#NAY8045A1<-RunPCA(NAY8045A1, verbose = FALSE)
#NAY8045A1<-RunUMAP(NAY8045A1, dims = 1:30, verbose = FALSE)
#NAY8045A1<-FindNeighbors(NAY8045A1, dims = 1:30, verbose = FALSE)
#NAY8045A1<-FindClusters(NAY8045A1, verbose = FALSE)
#DimPlot(NAY8045A1, label = TRUE) + NoLegend()

#Generate an individual metadata template for each library, populate .csv with all pertinent criteria
#############
metatest<-read.csv(file = "/Users/samnayler/Desktop/Disease_Seq/20200604_metatestA1.csv")
A2met<-as.data.frame(NAY8045A1@meta.data)


write.csv(A2met, file = "/Users/samnayler/Desktop/Disease_Seq/20200617_NAY8045A1md.csv")
sample_meta <- read.csv(file="/Users/samnayler/Desktop/Disease_Seq/20200604_metatestA1.csv", sep=",")
cell_meta <- read.csv(file="/Users/samnayler/Desktop/Disease_Seq/20200617_NAY8045A1md.csv")
 
meta_2 <- merge(cell_meta, sample_meta, by.x="hash.ID", by.y="hash.ID",all.x=T)
 
meta_3 <- meta_2[order(meta_2$X),]
rownames(meta_3)<-meta_3$X 

NAY8045A1@meta.data<-meta_3
############
#This works but the merge will be the test
levels(NAY8045A1@meta.data$HTO_classification)

##  [1] "BC21" "BC22" "BC23" "BC24" "BC25" "BC26" "BC27" "BC28" "BC29" "BC30"
## [11] "BC31" "BC32"

levels(NAY8045A1@meta.data$HTO_classification.global)

## [1] "Singlet"

levels(NAY8045A1@meta.data$hash.ID)

##  [1] "BC21"    "BC22"    "BC23"    "BC24"    "BC25"    "BC26"    "BC27"   
##  [8] "BC28"    "BC29"    "BC30"    "BC31"    "BC32"    "Doublet"

#These provide different readouts before after metadata incorporation. CHECK

#Processing currently commented out in favour of integrated
#NAY8045A1<-PercentageFeatureSet(NAY8045A1, pattern ="^MT-", col.name = "percent.mt")
#NAY8045A1<-SCTransform(NAY8045A1, vars.to.regress = "percent.mt", verbose = FALSE)
#NAY8045A1<-RunPCA(NAY8045A1, verbose = FALSE)
#NAY8045A1<-RunUMAP(NAY8045A1, dims = 1:30, verbose = FALSE)
#NAY8045A1<-FindNeighbors(NAY8045A1, dims = 1:30, verbose = FALSE)
#NAY8045A1<-FindClusters(NAY8045A1, verbose = FALSE)
#DimPlot(NAY8045A1, label = TRUE) + NoLegend()
#Need to do elbow plots to tinker with UMAP output

NAY8045A2

## An object of class Seurat 
## 33550 features across 10657 samples within 2 assays 
## Active assay: RNA (33538 features, 0 variable features)
##  1 other assay present: HTO

#An object of class Seurat 
#33550 features across 8977 samples within 2 assays 
#Active assay: RNA (33538 features)
#1 other assay present: HTO
head(NAY8045A2@meta.data$hash.ID)

## [1] BC32 BC35 BC38 BC29 BC33 BC32
## 13 Levels: BC29 BC30 BC31 BC32 BC33 BC34 BC35 BC36 BC37 BC38 BC39 ... Doublet

#BC29-BC40
VlnPlot(NAY8045A2, features = 'percent.mito', group.by ="hash.ID")

#Reformat metadata to include new layers
metatestA2<-read.csv(file = "/Users/samnayler/Desktop/Disease_Seq/20200604_metatestA2.csv")

metatronA2<-as.data.frame(NAY8045A2@meta.data)


######
#Test new method
write.csv(metatronA2, file = "/Users/samnayler/Desktop/Disease_Seq/20200617_NAY8045A2md.csv")
sample_meta2 <- read.csv(file="/Users/samnayler/Desktop/Disease_Seq/20200604_metatestA2.csv", sep=",")
cell_meta2 <- read.csv(file="/Users/samnayler/Desktop/Disease_Seq/20200617_NAY8045A2md.csv")
 
meta_2 <- merge(cell_meta2, sample_meta2, by.x="hash.ID", by.y="hash.ID",all.x=T)
 
meta_3 <- meta_2[order(meta_2$X),]
rownames(meta_3)<-meta_3$X

NAY8045A2@meta.data<-meta_3
#This works but the merge will be the test

#Processing currently commented out in favour of integrated
#NAY8045A2<-PercentageFeatureSet(NAY8045A2, pattern ="^MT-", col.name = "percent.mt")
#NAY8045A2<-SCTransform(NAY8045A2, vars.to.regress = "percent.mt", verbose = FALSE)
#NAY8045A2<-RunPCA(NAY8045A2, verbose = FALSE)
#NAY8045A2<-RunUMAP(NAY8045A2, dims = 1:30, verbose = FALSE)
#NAY8045A2<-FindNeighbors(NAY8045A2, dims = 1:30, verbose = FALSE)
#NAY8045A2<-FindClusters(NAY8045A2, verbose = FALSE)
#DimPlot(NAY8045A2, label = TRUE) + NoLegend()


#gene <- "DYNLRB2"
#g<-VlnPlot(NAY8045A2, features = gene, group.by ="genotype")
#h<-VlnPlot(NAY8045A2, features = gene, group.by ="hash.ID")
#i<-VlnPlot(NAY8045A2, features = gene, group.by ="line")

#plot_grid(g,h,i)

#I would need to reload all the objects and set up the metadata but not yet run the SCT normalization
#START INTEGRATED WORKFLOW HERE
#may need to percentagefeatureset

head(NAY8045A1@meta.data)

##                  hash.ID                X orig.ident nCount_RNA nFeature_RNA
## AAACCCACAAATCCCA    BC24 AAACCCACAAATCCCA    P200064       2348         1043
## AAACCCACAAGCGAGT    BC24 AAACCCACAAGCGAGT    P200064       4591         1515
## AAACCCACAAGGTTGG    BC28 AAACCCACAAGGTTGG    P200064       2002         1105
## AAACCCAGTACGTGTT    BC28 AAACCCAGTACGTGTT    P200064       1380          617
## AAACCCAGTAGCGTTT    BC23 AAACCCAGTAGCGTTT    P200064       2803         1625
## AAACCCAGTGAATTGA    BC28 AAACCCAGTGAATTGA    P200064       1995         1227
##                  gex.sequencing.id gex.library.id percent.mito percent.ribo
## AAACCCACAAATCCCA         NAY8045A1      NAY8045A1  0.012350937    0.3036627
## AAACCCACAAGCGAGT         NAY8045A1      NAY8045A1  0.019167937    0.3912002
## AAACCCACAAGGTTGG         NAY8045A1      NAY8045A1  0.008991009    0.2547453
## AAACCCAGTACGTGTT         NAY8045A1      NAY8045A1  0.005797101    0.2971014
## AAACCCAGTAGCGTTT         NAY8045A1      NAY8045A1  0.011773100    0.1155904
## AAACCCAGTGAATTGA         NAY8045A1      NAY8045A1  0.020050125    0.1117794
##                  hto.sequencing.id hto.library.id nCount_HTO nFeature_HTO
## AAACCCACAAATCCCA         NAY8045A5      NAY8045A5       5960           12
## AAACCCACAAGCGAGT         NAY8045A5      NAY8045A5       5700           12
## AAACCCACAAGGTTGG         NAY8045A5      NAY8045A5       3989           12
## AAACCCAGTACGTGTT         NAY8045A5      NAY8045A5       2660           12
## AAACCCAGTAGCGTTT         NAY8045A5      NAY8045A5       5216           12
## AAACCCAGTGAATTGA         NAY8045A5      NAY8045A5       2596           12
##                  HTO_classification HTO_classification.global age genotype
## AAACCCACAAATCCCA               BC24                   Singlet  90       wt
## AAACCCACAAGCGAGT               BC24                   Singlet  90       wt
## AAACCCACAAGGTTGG               BC28                   Singlet  60       wt
## AAACCCAGTACGTGTT               BC28                   Singlet  60       wt
## AAACCCAGTAGCGTTT               BC23                   Singlet  90       wt
## AAACCCAGTGAATTGA               BC28                   Singlet  60       wt
##                  gender  line rep clone pseudonym lineclone treatment cohort
## AAACCCACAAATCCCA    Fem AHO17   1    c3   Aragorn   AHO17c3      stim      1
## AAACCCACAAGCGAGT    Fem AHO17   1    c3   Aragorn   AHO17c3      stim      1
## AAACCCACAAGGTTGG    Fem AHO17   2    c3   Aragorn   AHO17c3      ctrl      1
## AAACCCAGTACGTGTT    Fem AHO17   2    c3   Aragorn   AHO17c3      ctrl      1
## AAACCCAGTAGCGTTT    Fem AHO17   3    c3   Aragorn   AHO17c3      ctrl      1
## AAACCCAGTGAATTGA    Fem AHO17   2    c3   Aragorn   AHO17c3      ctrl      1
##                  differentiation pool
## AAACCCACAAATCCCA            CD29    1
## AAACCCACAAGCGAGT            CD29    1
## AAACCCACAAGGTTGG            CD32    1
## AAACCCAGTACGTGTT            CD32    1
## AAACCCAGTAGCGTTT            CD29    1
## AAACCCAGTGAATTGA            CD32    1

dim(NAY8045A1@meta.data)

## [1] 8746   27

head(NAY8045A2@meta.data)

##                  hash.ID                X orig.ident nCount_RNA nFeature_RNA
## AAACCCACACGATAGG    BC32 AAACCCACACGATAGG    P200064       1773          962
## AAACCCACATTGTGCA    BC35 AAACCCACATTGTGCA    P200064       3196         1260
## AAACCCAGTAGAATGT    BC38 AAACCCAGTAGAATGT    P200064       1858          798
## AAACCCATCATTCTTG    BC29 AAACCCATCATTCTTG    P200064      15542         4135
## AAACCCATCGAACGGA    BC33 AAACCCATCGAACGGA    P200064        579          315
## AAACCCATCGTGCATA    BC32 AAACCCATCGTGCATA    P200064       5939         1602
##                  gex.sequencing.id gex.library.id percent.mito percent.ribo
## AAACCCACACGATAGG         NAY8045A2      NAY8045A2  0.020868584    0.2340666
## AAACCCACATTGTGCA         NAY8045A2      NAY8045A2  0.007196496    0.3604506
## AAACCCAGTAGAATGT         NAY8045A2      NAY8045A2  0.011840689    0.4165770
## AAACCCATCATTCTTG         NAY8045A2      NAY8045A2  0.008750483    0.2531206
## AAACCCATCGAACGGA         NAY8045A2      NAY8045A2  0.015544041    0.4421416
## AAACCCATCGTGCATA         NAY8045A2      NAY8045A2  0.010776225    0.3778414
##                  hto.sequencing.id hto.library.id nCount_HTO nFeature_HTO
## AAACCCACACGATAGG         NAY8045A6      NAY8045A6       4356           12
## AAACCCACATTGTGCA         NAY8045A6      NAY8045A6       5542           12
## AAACCCAGTAGAATGT         NAY8045A6      NAY8045A6       3708           12
## AAACCCATCATTCTTG         NAY8045A6      NAY8045A6      15300           12
## AAACCCATCGAACGGA         NAY8045A6      NAY8045A6       5658           12
## AAACCCATCGTGCATA         NAY8045A6      NAY8045A6       4579           12
##                  HTO_classification HTO_classification.global age genotype
## AAACCCACACGATAGG               BC32                   Singlet  60       wt
## AAACCCACATTGTGCA               BC35                   Singlet  60       AT
## AAACCCAGTAGAATGT               BC38                   Singlet  60       AT
## AAACCCATCATTCTTG               BC29                   Singlet  60       AT
## AAACCCATCGAACGGA               BC33                   Singlet  60       wt
## AAACCCATCGTGCATA               BC32                   Singlet  60       wt
##                  gender  line rep clone pseudonym lineclone treatment cohort
## AAACCCACACGATAGG   Male    NI   1    c8   Gandalf      NIc8      ctrl      1
## AAACCCACATTGTGCA    Fem AT210   1    c3     Gimli   AT210c3      ctrl      1
## AAACCCAGTAGAATGT      x   AT1   1   c11     Merry    AT1c11      ctrl      1
## AAACCCATCATTCTTG    Fem AT210   1    c2     Frodo   AT210c2      ctrl      1
## AAACCCATCGAACGGA   Male    NI   2    c8   Gandalf      NIc8      ctrl      1
## AAACCCATCGTGCATA   Male    NI   1    c8   Gandalf      NIc8      ctrl      1
##                  differentiation pool
## AAACCCACACGATAGG            CD32    2
## AAACCCACATTGTGCA            CD32    2
## AAACCCAGTAGAATGT            CD32    2
## AAACCCATCATTCTTG            CD32    2
## AAACCCATCGAACGGA            CD32    2
## AAACCCATCGTGCATA            CD32    2

dim(NAY8045A2@meta.data)

## [1] 10657    27

#Need to filter out doublets for NAY8045A1, NAY8045A2
table(NAY8045A1@meta.data$HTO_classification.global)

## 
## Singlet 
##    8746

table(NAY8045A2@meta.data$HTO_classification.global)

## 
## Singlet 
##   10657

table(NAY8045A1@meta.data$hash.ID)

## 
##    BC21    BC22    BC23    BC24    BC25    BC26    BC27    BC28    BC29    BC30 
##     383     268     626     447     495     805    1764    1102    1426     103 
##    BC31    BC32 Doublet 
##      43     214    1070

table(NAY8045A2@meta.data$hash.ID)

## 
##    BC29    BC30    BC31    BC32    BC33    BC34    BC35    BC36    BC37    BC38 
##    1022     305      36     701    1220     454     496     137     318    3946 
##    BC39    BC40 Doublet 
##     722     937     363

#Make sure to update to dev
#devtools::install_github(repo = "satijalab/seurat", ref = "develop")
#Note only run thise after reading in the non-normalized objects

#disseq.list.test <- c(NAY8045A1, NAY8045A2)
#Should come back to subset out doublets
#Want to subset only hashID singlets
Idents(NAY8045A1) <-"hash.ID"
Idents(NAY8045A2) <-"hash.ID"



NAY8045A1_s<-subset(NAY8045A1, idents = "Doublet", invert = TRUE)
NAY8045A2_s<-subset(NAY8045A2, idents = "Doublet", invert = TRUE)
VlnPlot(NAY8045A1_s, features ="percent.mito", group.by = "hash.ID")

disseq.list.test <- c(NAY8045A1_s, NAY8045A2_s)


for (i in 1:length(disseq.list.test)) {
   disseq.list.test[[i]] <- SCTransform(disseq.list.test[[i]], verbose = FALSE)
}

## Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

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## Warning in sqrt(1/i): NaNs produced

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disseq.features <- SelectIntegrationFeatures(object.list = disseq.list.test, nfeatures = 3000)
options(future.globals.maxSize = 4000 * 1024^2)

disseq.list.test<- PrepSCTIntegration(object.list = disseq.list.test, anchor.features = disseq.features, verbose = FALSE)

disseq.anchors.test <- FindIntegrationAnchors(object.list = disseq.list.test, normalization.method = "SCT", anchor.features = disseq.features, verbose = FALSE)

## Warning in CheckDuplicateCellNames(object.list = object.list): Some cell names
## are duplicated across objects provided. Renaming to enforce unique cell names.

disseq.integrated.test <- IntegrateData(anchorset = disseq.anchors.test, normalization.method = "SCT", verbose = FALSE)

## Warning: Adding a command log without an assay associated with it

df<-as.data.frame(disseq.integrated.test@meta.data)
write.csv(df, file = "/Users/samnayler/Desktop/md.csv")

disseq.integrated.test <- RunPCA(disseq.integrated.test, verbose = FALSE)
disseq.integrated.test <- RunUMAP(disseq.integrated.test, dims = 1:20)

## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session

## 12:26:45 UMAP embedding parameters a = 0.9922 b = 1.112

## 12:26:45 Read 17970 rows and found 20 numeric columns

## 12:26:45 Using Annoy for neighbor search, n_neighbors = 30

## 12:26:45 Building Annoy index with metric = cosine, n_trees = 50

## 0%   10   20   30   40   50   60   70   80   90   100%

## [----|----|----|----|----|----|----|----|----|----|

## **************************************************|
## 12:26:48 Writing NN index file to temp file /var/folders/jy/hkdv9ktd6pl627fw14fvwn500000gq/T//RtmpzzANrV/file173285f24b5
## 12:26:48 Searching Annoy index using 1 thread, search_k = 3000
## 12:26:53 Annoy recall = 100%
## 12:26:53 Commencing smooth kNN distance calibration using 1 thread
## 12:26:54 Initializing from normalized Laplacian + noise
## 12:26:55 Commencing optimization for 200 epochs, with 797080 positive edges
## 12:27:05 Optimization finished

a<-DimPlot(disseq.integrated.test, group.by = c("line"), combine = TRUE)
disseq.integrated.test <- RunPCA(disseq.integrated.test, verbose = FALSE)
disseq.integrated.test <- RunUMAP(disseq.integrated.test, dims = 1:30)

## 12:27:22 UMAP embedding parameters a = 0.9922 b = 1.112
## 12:27:22 Read 17970 rows and found 30 numeric columns
## 12:27:22 Using Annoy for neighbor search, n_neighbors = 30
## 12:27:22 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 12:27:25 Writing NN index file to temp file /var/folders/jy/hkdv9ktd6pl627fw14fvwn500000gq/T//RtmpzzANrV/file17321e62eb5e
## 12:27:25 Searching Annoy index using 1 thread, search_k = 3000
## 12:27:30 Annoy recall = 100%
## 12:27:30 Commencing smooth kNN distance calibration using 1 thread
## 12:27:31 Initializing from normalized Laplacian + noise
## 12:27:32 Commencing optimization for 200 epochs, with 810032 positive edges
## 12:27:42 Optimization finished

b<-DimPlot(disseq.integrated.test, group.by = c("line"), combine = TRUE)
disseq.integrated.test <- RunPCA(disseq.integrated.test, verbose = FALSE)
disseq.integrated.test <- RunUMAP(disseq.integrated.test, dims = 1:50)

## 12:27:58 UMAP embedding parameters a = 0.9922 b = 1.112
## 12:27:58 Read 17970 rows and found 50 numeric columns
## 12:27:58 Using Annoy for neighbor search, n_neighbors = 30
## 12:27:58 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 12:28:01 Writing NN index file to temp file /var/folders/jy/hkdv9ktd6pl627fw14fvwn500000gq/T//RtmpzzANrV/file1732704a77e7
## 12:28:01 Searching Annoy index using 1 thread, search_k = 3000
## 12:28:07 Annoy recall = 100%
## 12:28:07 Commencing smooth kNN distance calibration using 1 thread
## 12:28:08 Initializing from normalized Laplacian + noise
## 12:28:09 Commencing optimization for 200 epochs, with 830604 positive edges
## 12:28:19 Optimization finished

c<-DimPlot(disseq.integrated.test, group.by = c("line"), combine = TRUE)

plot_grid(a,b,c, nrow = 3, labels = c("20 dims", "30 dims", "50 dims"))

#30 dims looks good

#saveRDS(disseq.integrated.test, file = "/Users/samnayler/Desktop/Disease_Seq/20200618_British_Integrated.rds")
#disseq.integrated.test<-readRDS(file = "/Users/samnayler/Desktop/Disease_Seq/20200618_British_Integrated.rds")
#comeback



#Make a pipe function here for iterating through colnames(disseq.integrated.test@meta.data)
a<-DimPlot(disseq.integrated.test, group.by = c("line"), combine = TRUE)
b<-DimPlot(disseq.integrated.test, group.by = c("differentiation"), combine = TRUE)
c<-DimPlot(disseq.integrated.test, group.by = c("genotype"), combine = TRUE)
d<-DimPlot(disseq.integrated.test, group.by = c("hash.ID"), combine = TRUE)
e<-DimPlot(disseq.integrated.test, group.by = c("age"), combine = TRUE)
f<-DimPlot(disseq.integrated.test, group.by = c("lineclone"), combine = TRUE)
g<-DimPlot(disseq.integrated.test, group.by = c("nCount_RNA"), combine = TRUE)
h<-DimPlot(disseq.integrated.test, group.by = c("pool"), combine = TRUE)
i<-DimPlot(disseq.integrated.test, group.by = c("pseudonym"), combine = TRUE)
j<-DimPlot(disseq.integrated.test, group.by = c("cohort"), combine = TRUE)
k<-DimPlot(disseq.integrated.test, group.by = c("gender"), combine = TRUE)
l<-DimPlot(disseq.integrated.test, group.by = c("treatment"), combine = TRUE)

qcplots1<-plot_grid(a,b,c,d, nrow = 4, labels = c("line", "differentiation", "genotype", "hash.ID"))
qcplots2<-plot_grid(e,f,g,h, nrow = 4, labels = c("age", "lineclone", "nCount_RNA", "pool"))
qcplots3<-plot_grid(i,j,k,l, nrow = 4, labels = c("pseudonym", "cohort", "gender", "treatment"))
qcplots1

qcplots2

qcplots3

#Here I want to  have a quick look at clustering resolution in disseq.integrated
#Errors here;
#Provided graph.name not present in Seurat object
#Try FindNeighbours first
disseq.integrated.test<-FindNeighbors(object = disseq.integrated.test)

## Computing nearest neighbor graph

## Computing SNN

disseq.integrated.test<-FindClusters(disseq.integrated.test, resolution = 0.1)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 17970
## Number of edges: 585422
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9585
## Number of communities: 8
## Elapsed time: 2 seconds

disseq.integrated.test<-FindClusters(disseq.integrated.test, resolution = 0.2)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 17970
## Number of edges: 585422
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9420
## Number of communities: 9
## Elapsed time: 2 seconds

disseq.integrated.test<-FindClusters(disseq.integrated.test, resolution = 0.3)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 17970
## Number of edges: 585422
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9282
## Number of communities: 9
## Elapsed time: 2 seconds

disseq.integrated.test<-FindClusters(disseq.integrated.test, resolution = 0.4)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 17970
## Number of edges: 585422
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9186
## Number of communities: 12
## Elapsed time: 2 seconds

disseq.integrated.test<-FindClusters(disseq.integrated.test, resolution = 0.5)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 17970
## Number of edges: 585422
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9097
## Number of communities: 15
## Elapsed time: 3 seconds

disseq.integrated.test<-FindClusters(disseq.integrated.test, resolution = 0.6)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 17970
## Number of edges: 585422
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9020
## Number of communities: 17
## Elapsed time: 2 seconds

disseq.integrated.test<-FindClusters(disseq.integrated.test, resolution = 0.7)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 17970
## Number of edges: 585422
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8955
## Number of communities: 20
## Elapsed time: 2 seconds

disseq.integrated.test<-FindClusters(disseq.integrated.test, resolution = 0.8)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 17970
## Number of edges: 585422
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8895
## Number of communities: 20
## Elapsed time: 3 seconds

disseq.integrated.test<-FindClusters(disseq.integrated.test, resolution = 0.9)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 17970
## Number of edges: 585422
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8849
## Number of communities: 22
## Elapsed time: 3 seconds

disseq.integrated.test<-FindClusters(disseq.integrated.test, resolution = 1.0)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 17970
## Number of edges: 585422
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8795
## Number of communities: 22
## Elapsed time: 3 seconds

clusts0.1<-disseq.integrated.test@meta.data$integrated_snn_res.0.1
clusts0.2<-disseq.integrated.test@meta.data$integrated_snn_res.0.2
clusts0.3<-disseq.integrated.test@meta.data$integrated_snn_res.0.3
clusts0.4<-disseq.integrated.test@meta.data$integrated_snn_res.0.4
clusts0.5<-disseq.integrated.test@meta.data$integrated_snn_res.0.5
clusts0.6<-disseq.integrated.test@meta.data$integrated_snn_res.0.6
clusts0.7<-disseq.integrated.test@meta.data$integrated_snn_res.0.7
clusts0.8<-disseq.integrated.test@meta.data$integrated_snn_res.0.8
clusts0.9<-disseq.integrated.test@meta.data$integrated_snn_res.0.9
clusts1.0<-disseq.integrated.test@meta.data$integrated_snn_res.1



clusts<-data.frame(clusts0.1, clusts0.2, clusts0.3, clusts0.4,clusts0.5,clusts0.6,clusts0.7,clusts0.8,clusts0.9,clusts1.0)
clustree(clusts, prefix = "clusts")

res1<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.1", label = TRUE) + NoLegend()

## Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
## Please use `as_label()` or `as_name()` instead.
## This warning is displayed once per session.

res2<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.2", label = TRUE)  + NoLegend()
res3<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.3", label = TRUE)  + NoLegend()
res4<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.4", label = TRUE)  + NoLegend()
res5<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.5", label = TRUE)  + NoLegend()
res6<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.6", label = TRUE)  + NoLegend()
res7<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.7", label = TRUE)  + NoLegend()
res8<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.8", label = TRUE)  + NoLegend()
res9<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.9", label = TRUE)  + NoLegend()
res10<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.1", label = TRUE)  + NoLegend()

#allres<-plot_grid(res1, res2, res3, res4, res5, res6, res7, res8, res9, res10)
allres<-plot_grid(res1, res2, res3, res4, res5, res6, res7, res8, res9, res10, nrow = 2)
allres

#Lets start with 0.4 as we have 12 populations (similar to pilot study)

#gene <- "DYNLRB2"
#g<-VlnPlot(NAY8045A1_b, features = gene, group.by ="genotype")
#h<-VlnPlot(NAY8045A1_b, features = gene, group.by ="hash.ID")
#plot_grid(g,h)


#DefaultAssay(NAY8045A1) <- "SCT"


#carterlist<-c("SPARC", "ID1", "ID3", "MSX1", "TMEM163", "MEIS2", "LHX9", "GREM2", "MFAP4", "NEUROD1", "ATOH1", "BARHL1", "PPP2R2C", "LHX5", "LHX1", "GAD2", "GAD1", "FOXP2", "SLC32A1", "CALB1", "PAX2", "LBX1", "HOPX", "TIMP4", "NDRG2", "GDF10", "SOX9", "SOX10", "SLC1A3", "SPARCL1", "VTN", "BGN", "IGFBP7", "DYNLRB2", "DCN", "COL3A1", "COL1A2", "KRT18", "COLEC12", "MATN4", "ISL1", "SNCG", "DLK1")
#DotPlot(NAY8045A1, features = carterlist, dot.scale = 8) + RotatedAxis()

#samlist<-c("PCP4", "HTR2C", "SERPINF1", "IGF1", "TUBB2B", "TUBA1A", "MAPT", "VAMP2", "RAB3A", "NSG1", "STMN2", "SPARCL1", "ANXA2", "TPBG", "IGFBP5", "WNT2B", "PTN", "GPM6B", "C1orf61", "VIM", "CAV1", "GDF10", "PKMYT1", "TK1", "RRM2", "NHLH1", "CNTN2", "DAPL1", "ATOH1", "TCF4", "MIAT", "TRIB3", "HSPA9", "DDIT3", "NUPR1", "EIF1", "KCNG1", "RSPO1", "RSPO2", "CNTNAP2", "MASP1", "PLS3", "ASPM", "TTK", "DLGAP5", "CENPA", "TROAP", "MKI67", "TCF7L2", "TMSB4X", "NEUROG2", "DPYSL3", "NNAT", "OLIG3", "MIR4435-2HG", "HIST1H4H", "HIST1H2AC", "GADD45B", "HIST1H2AE", "C11orf88", "ARMC3", "CAPSL", "CFAP126", "MAP3K19", "ANKRD66")

#DotPlot(NAY8045A1, features = samlist, dot.scale = 8) + RotatedAxis()

#VlnPlot (NAY8045A1, features = "KIRREL2", pt.size = 0.2, ncol =2)

#disseq.integrated.test at a resolution of 0.4 looks like a good place to start
#Could try and write a for loop here. Establish base code first
#PLAY WITH LOGFC AND MINPCT

#res4<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.4", label = TRUE)  + NoLegend()

Idents(disseq.integrated.test) <-"integrated_snn_res.0.4"

brit_04res_12pop_Pop0markers <- FindMarkers (disseq.integrated.test, ident.1 = "0", ident.2 = NULL, only.pos = TRUE, logfc.threshold = 3, mic.pct = 0.25)

head(brit_04res_12pop_Pop0markers)

##           p_val avg_logFC pct.1 pct.2 p_val_adj
## TCEAL5        0  4.503401 0.146 0.149         0
## C14orf39      0  4.138245 0.207 0.099         0
## HIST2H2AC     0  3.929737 0.285 0.154         0
## CENPN         0  3.916560 0.143 0.152         0
## IPO9-AS1      0  3.636377 0.048 0.124         0
## PARPBP        0  3.470442 0.071 0.121         0

Pop0<-rownames(brit_04res_12pop_Pop0markers)


FeaturePlot(disseq.integrated.test, features = Pop0[1:12], max.cutoff = 3, cols = c("grey", "red"), pt.size = 0.1)

P0 <- WhichCells(disseq.integrated.test, idents = "0")
DimPlot(disseq.integrated.test, group.by =  'integrated_snn_res.0.4', pt.size = 0.1, label= TRUE, cells.highlight = (P0))

a<-VlnPlot(disseq.integrated.test, features = Pop0[1], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
b<-VlnPlot(disseq.integrated.test, features = Pop0[2], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
c<-VlnPlot(disseq.integrated.test, features = Pop0[3], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
d<-VlnPlot(disseq.integrated.test, features = Pop0[4], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
e<-VlnPlot(disseq.integrated.test, features = Pop0[5], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
f<-VlnPlot(disseq.integrated.test, features = Pop0[6], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

markers0<-plot_grid(a,b,c,d,e,f, nrow = 3)
#This doesnt quite look right
#Validate with feature plots and tune up by adjusting logfc.thresholds

#disseq.integrated.test at a resolution of 0.4 looks like a good place to start
#Could try and write a for loop here. Establish base code first

#res4<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.4", label = TRUE)  + NoLegend()

Idents(disseq.integrated.test) <-"integrated_snn_res.0.4"

brit_04res_12pop_Pop1markers <- FindMarkers (disseq.integrated.test, ident.1 = "1", ident.2 = NULL, only.pos = TRUE, logfc.threshold = 3, mic.pct = 0.25)

head(brit_04res_12pop_Pop1markers)

##         p_val avg_logFC pct.1 pct.2 p_val_adj
## GRIA2       0  8.608579 0.803 0.068         0
## CLASP2      0  7.766760 0.788 0.166         0
## GPM6A       0  7.685784 0.643 0.175         0
## HIP1R       0  7.541367 0.628 0.082         0
## PRKAR2B     0  7.164466 0.691 0.152         0
## XPR1        0  7.071430 0.638 0.125         0

Pop1<-rownames(brit_04res_12pop_Pop1markers)


FeaturePlot(disseq.integrated.test, features = Pop1[1:12], max.cutoff = 3, cols = c("grey", "red"), pt.size = 0.1)

P1 <- WhichCells(disseq.integrated.test, idents = "1")
DimPlot(disseq.integrated.test, group.by =  'integrated_snn_res.0.4', pt.size = 0.1, label= TRUE, cells.highlight = (P1))

a<-VlnPlot(disseq.integrated.test, features = Pop1[1], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
b<-VlnPlot(disseq.integrated.test, features = Pop1[2], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
c<-VlnPlot(disseq.integrated.test, features = Pop1[3], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
d<-VlnPlot(disseq.integrated.test, features = Pop1[4], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
e<-VlnPlot(disseq.integrated.test, features = Pop1[5], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
f<-VlnPlot(disseq.integrated.test, features = Pop1[6], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

markers1<-plot_grid(a,b,c,d,e,f, nrow = 3)
#This doesnt quite look right
#Validate with feature plots and tune up by adjusting logfc.thresholds

#res4<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.4", label = TRUE)  + NoLegend()

#Idents(disseq.integrated.test) <-"integrated_snn_res.0.4"

brit_04res_12pop_Pop2markers <- FindMarkers (disseq.integrated.test, ident.1 = "2", ident.2 = NULL, only.pos = TRUE, logfc.threshold = 3, mic.pct = 0.25)

head(brit_04res_12pop_Pop2markers)

##         p_val avg_logFC pct.1 pct.2 p_val_adj
## DCC         0  4.911408 0.545 0.136         0
## TAGLN3      0  4.845753 0.645 0.156         0
## IGFBPL1     0  3.835550 0.631 0.151         0
## GNG3        0  3.436278 0.644 0.160         0
## MLLT11      0  3.401237 0.721 0.245         0
## TUBA1A      0  3.221319 0.880 0.308         0

Pop2<-rownames(brit_04res_12pop_Pop2markers)


FeaturePlot(disseq.integrated.test, features = Pop2[1:12], max.cutoff = 3, cols = c("grey", "red"), pt.size = 0.1)

P2 <- WhichCells(disseq.integrated.test, idents = "2")
DimPlot(disseq.integrated.test, group.by =  'integrated_snn_res.0.4', pt.size = 0.1, label= TRUE, cells.highlight = (P2))

a<-VlnPlot(disseq.integrated.test, features = Pop2[1], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
b<-VlnPlot(disseq.integrated.test, features = Pop2[2], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
c<-VlnPlot(disseq.integrated.test, features = Pop2[3], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
d<-VlnPlot(disseq.integrated.test, features = Pop2[4], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
e<-VlnPlot(disseq.integrated.test, features = Pop2[5], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
f<-VlnPlot(disseq.integrated.test, features = Pop2[6], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

markers2<-plot_grid(a,b,c,d,e,f, nrow = 3)


#STOPPED HERE THURS. RESUME WITH OTHER POPULATIONS AND RESOLUTIONS

#res4<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.4", label = TRUE)  + NoLegend()

#Idents(disseq.integrated.test) <-"integrated_snn_res.0.4"

brit_04res_12pop_Pop3markers <- FindMarkers (disseq.integrated.test, ident.1 = "3", ident.2 = NULL, only.pos = TRUE, logfc.threshold = 3, mic.pct = 0.25)

head(brit_04res_12pop_Pop3markers)

##        p_val avg_logFC pct.1 pct.2 p_val_adj
## TPX2       0 12.544811 0.851 0.069         0
## TOP2A      0 12.174502 0.837 0.063         0
## HMGB2      0 11.137169 0.927 0.166         0
## CKAP2      0 11.120462 0.804 0.158         0
## UBE2C      0  9.912698 0.747 0.078         0
## NUSAP1     0  9.207991 0.890 0.066         0

Pop3<-rownames(brit_04res_12pop_Pop3markers)


FeaturePlot(disseq.integrated.test, features = Pop3[1:12], max.cutoff = 3, cols = c("grey", "red"), pt.size = 0.1)

P3 <- WhichCells(disseq.integrated.test, idents = "3")
DimPlot(disseq.integrated.test, group.by =  'integrated_snn_res.0.4', pt.size = 0.1, label= TRUE, cells.highlight = (P3))

a<-VlnPlot(disseq.integrated.test, features = Pop3[1], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
b<-VlnPlot(disseq.integrated.test, features = Pop3[2], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
c<-VlnPlot(disseq.integrated.test, features = Pop3[3], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
d<-VlnPlot(disseq.integrated.test, features = Pop3[4], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
e<-VlnPlot(disseq.integrated.test, features = Pop3[5], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
f<-VlnPlot(disseq.integrated.test, features = Pop3[6], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

markers3<-plot_grid(a,b,c,d,e,f, nrow = 3)

#res4<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.4", label = TRUE)  + NoLegend()

#Idents(disseq.integrated.test) <-"integrated_snn_res.0.4"

brit_04res_12pop_Pop4markers <- FindMarkers (disseq.integrated.test, ident.1 = "4", ident.2 = NULL, only.pos = TRUE, logfc.threshold = 3, mic.pct = 0.25)

head(brit_04res_12pop_Pop4markers)

##                  p_val avg_logFC pct.1 pct.2     p_val_adj
## NDUFA4L2 1.942665e-136  3.049711 0.570 0.133 5.827996e-133
## ENHO      1.841063e-32  3.124229 0.424 0.212  5.523188e-29

Pop4<-rownames(brit_04res_12pop_Pop4markers)


FeaturePlot(disseq.integrated.test, features = Pop4[1:12], max.cutoff = 3, cols = c("grey", "red"), pt.size = 0.1)

## Warning in FetchData(object = object, vars = c(dims, "ident", features), : The
## following requested variables were not found: NA

P4 <- WhichCells(disseq.integrated.test, idents = "4")
DimPlot(disseq.integrated.test, group.by =  'integrated_snn_res.0.4', pt.size = 0.1, label= TRUE, cells.highlight = (P4))

a<-VlnPlot(disseq.integrated.test, features = Pop4[1], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
b<-VlnPlot(disseq.integrated.test, features = Pop4[2], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)


markers4<-plot_grid(a,b, nrow = 3)

#res4<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.4", label = TRUE)  + NoLegend()

#Idents(disseq.integrated.test) <-"integrated_snn_res.0.4"

brit_04res_12pop_Pop5markers <- FindMarkers (disseq.integrated.test, ident.1 = "5", ident.2 = NULL, only.pos = TRUE, logfc.threshold = 3, mic.pct = 0.25)

head(brit_04res_12pop_Pop5markers)

##               p_val avg_logFC pct.1 pct.2     p_val_adj
## GPM6B  0.000000e+00  8.304168 0.729 0.233  0.000000e+00
## NTRK2  0.000000e+00  5.898902 0.742 0.100  0.000000e+00
## MSX1   0.000000e+00  5.272537 0.687 0.124  0.000000e+00
## WLS    0.000000e+00  4.114912 0.841 0.174  0.000000e+00
## RFX4   0.000000e+00  3.524944 0.686 0.088  0.000000e+00
## RSPO3 4.915881e-301  4.849127 0.537 0.055 1.474764e-297

Pop5<-rownames(brit_04res_12pop_Pop5markers)


FeaturePlot(disseq.integrated.test, features = Pop5[1:12], max.cutoff = 3, cols = c("grey", "red"), pt.size = 0.1)

P5 <- WhichCells(disseq.integrated.test, idents = "5")
DimPlot(disseq.integrated.test, group.by =  'integrated_snn_res.0.4', pt.size = 0.1, label= TRUE, cells.highlight = (P5))

a<-VlnPlot(disseq.integrated.test, features = Pop5[1], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
b<-VlnPlot(disseq.integrated.test, features = Pop5[2], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
c<-VlnPlot(disseq.integrated.test, features = Pop5[3], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
d<-VlnPlot(disseq.integrated.test, features = Pop5[4], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
e<-VlnPlot(disseq.integrated.test, features = Pop5[5], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
f<-VlnPlot(disseq.integrated.test, features = Pop5[6], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

markers5<-plot_grid(a,b,c,d,e,f, nrow = 3)

#This population could be RL due to strong WLS; should be checking Math1, Pax6, Lmx1a, and Tbr2

#res4<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.4", label = TRUE)  + NoLegend()

#Idents(disseq.integrated.test) <-"integrated_snn_res.0.4"

brit_04res_12pop_Pop6markers <- FindMarkers (disseq.integrated.test, ident.1 = "6", ident.2 = NULL, only.pos = TRUE, logfc.threshold = 3, mic.pct = 0.25)

head(brit_04res_12pop_Pop6markers)

##        p_val avg_logFC pct.1 pct.2 p_val_adj
## MFAP2      0  8.345477 0.774 0.186         0
## PRRX1      0  5.260173 0.780 0.125         0
## OGN        0  5.038797 0.748 0.060         0
## LUM        0  4.936380 0.740 0.089         0
## COL1A2     0  4.561986 0.847 0.095         0
## VCAN       0  4.271977 0.820 0.162         0

Pop6<-rownames(brit_04res_12pop_Pop6markers)


FeaturePlot(disseq.integrated.test, features = Pop6[1:12], max.cutoff = 3, cols = c("grey", "red"), pt.size = 0.1)

P6 <- WhichCells(disseq.integrated.test, idents = "6")
DimPlot(disseq.integrated.test, group.by =  'integrated_snn_res.0.4', pt.size = 0.1, label= TRUE, cells.highlight = (P6))

a<-VlnPlot(disseq.integrated.test, features = Pop6[1], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
b<-VlnPlot(disseq.integrated.test, features = Pop6[2], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
c<-VlnPlot(disseq.integrated.test, features = Pop6[3], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
d<-VlnPlot(disseq.integrated.test, features = Pop6[4], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
e<-VlnPlot(disseq.integrated.test, features = Pop6[5], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
f<-VlnPlot(disseq.integrated.test, features = Pop6[6], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

markers6<-plot_grid(a,b,c,d,e,f, nrow = 3)

#res4<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.4", label = TRUE)  + NoLegend()

#Idents(disseq.integrated.test) <-"integrated_snn_res.0.4"

brit_04res_12pop_Pop7markers <- FindMarkers (disseq.integrated.test, ident.1 = "7", ident.2 = NULL, only.pos = TRUE, logfc.threshold = 3, mic.pct = 0.25)

head(brit_04res_12pop_Pop7markers)

##                 p_val avg_logFC pct.1 pct.2     p_val_adj
## FTL     2.380676e-181  3.755484 0.709 0.360 7.142029e-178
## S100A11 1.439178e-173  4.699977 0.647 0.319 4.317534e-170
## DMKN    1.986280e-150  6.018385 0.472 0.128 5.958839e-147
## BST2    4.057543e-141  3.572893 0.452 0.161 1.217263e-137
## CEBPD   2.491625e-131  5.732466 0.453 0.162 7.474876e-128
## MECOM   5.831400e-126  4.250089 0.467 0.106 1.749420e-122

Pop7<-rownames(brit_04res_12pop_Pop7markers)


FeaturePlot(disseq.integrated.test, features = Pop7[1:12], max.cutoff = 3, cols = c("grey", "red"), pt.size = 0.1)

P7 <- WhichCells(disseq.integrated.test, idents = "7")
DimPlot(disseq.integrated.test, group.by =  'integrated_snn_res.0.4', pt.size = 0.1, label= TRUE, cells.highlight = (P7))

a<-VlnPlot(disseq.integrated.test, features = Pop7[1], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
b<-VlnPlot(disseq.integrated.test, features = Pop7[2], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
c<-VlnPlot(disseq.integrated.test, features = Pop7[3], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
d<-VlnPlot(disseq.integrated.test, features = Pop7[4], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
e<-VlnPlot(disseq.integrated.test, features = Pop7[5], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
f<-VlnPlot(disseq.integrated.test, features = Pop7[6], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

markers7<-plot_grid(a,b,c,d,e,f, nrow = 3)

#Should look at FTL in ATM/CTRL

#res4<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.4", label = TRUE)  + NoLegend()

#Idents(disseq.integrated.test) <-"integrated_snn_res.0.4"

brit_04res_12pop_Pop8markers <- FindMarkers (disseq.integrated.test, ident.1 = "8", ident.2 = NULL, only.pos = TRUE, logfc.threshold = 3, mic.pct = 0.25)

head(brit_04res_12pop_Pop8markers)

##        p_val avg_logFC pct.1 pct.2 p_val_adj
## HSPB1      0 11.580079 0.957 0.297         0
## YBX3       0 10.844412 0.854 0.256         0
## CFL2       0 10.259474 0.926 0.219         0
## ACTA1      0 10.084442 0.923 0.175         0
## TCEAL7     0  9.778730 0.832 0.220         0
## BIN1       0  8.997057 0.910 0.169         0

Pop8<-rownames(brit_04res_12pop_Pop8markers)


FeaturePlot(disseq.integrated.test, features = Pop8[1:12], max.cutoff = 3, cols = c("grey", "red"), pt.size = 0.1)

P8 <- WhichCells(disseq.integrated.test, idents = "8")
DimPlot(disseq.integrated.test, group.by =  'integrated_snn_res.0.4', pt.size = 0.1, label= TRUE, cells.highlight = (P8))

a<-VlnPlot(disseq.integrated.test, features = Pop8[1], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
b<-VlnPlot(disseq.integrated.test, features = Pop8[2], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
c<-VlnPlot(disseq.integrated.test, features = Pop8[3], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
d<-VlnPlot(disseq.integrated.test, features = Pop8[4], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
e<-VlnPlot(disseq.integrated.test, features = Pop8[5], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
f<-VlnPlot(disseq.integrated.test, features = Pop8[6], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

markers8<-plot_grid(a,b,c,d,e,f, nrow = 3)

#res4<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.4", label = TRUE)  + NoLegend()

#Idents(disseq.integrated.test) <-"integrated_snn_res.0.4"

brit_04res_12pop_Pop9markers <- FindMarkers (disseq.integrated.test, ident.1 = "9", ident.2 = NULL, only.pos = TRUE, logfc.threshold = 3, mic.pct = 0.25)

head(brit_04res_12pop_Pop9markers)

##                    p_val avg_logFC pct.1 pct.2     p_val_adj
## TTN         0.000000e+00  4.203622 0.817 0.152  0.000000e+00
## NEB        7.007070e-240  3.641761 0.671 0.171 2.102121e-236
## AC009948.3 5.907509e-216  3.602233 0.113 0.058 1.772253e-212
## CCDC141    4.847033e-213  3.309415 0.217 0.075 1.454110e-209
## ALPK3      7.096450e-213  5.173888 0.285 0.099 2.128935e-209
## MYH7       1.946356e-209  3.102196 0.105 0.056 5.839069e-206

Pop9<-rownames(brit_04res_12pop_Pop9markers)


FeaturePlot(disseq.integrated.test, features = Pop9[1:12], max.cutoff = 3, cols = c("grey", "red"), pt.size = 0.1)

P9 <- WhichCells(disseq.integrated.test, idents = "9")
DimPlot(disseq.integrated.test, group.by =  'integrated_snn_res.0.4', pt.size = 0.1, label= TRUE, cells.highlight = (P9))

a<-VlnPlot(disseq.integrated.test, features = Pop9[1], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
b<-VlnPlot(disseq.integrated.test, features = Pop9[2], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
c<-VlnPlot(disseq.integrated.test, features = Pop9[3], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
d<-VlnPlot(disseq.integrated.test, features = Pop9[4], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
e<-VlnPlot(disseq.integrated.test, features = Pop9[5], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
f<-VlnPlot(disseq.integrated.test, features = Pop9[6], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

markers9<-plot_grid(a,b,c,d,e,f, nrow = 3)

#res4<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.4", label = TRUE)  + NoLegend()

#Idents(disseq.integrated.test) <-"integrated_snn_res.0.4"

brit_04res_12pop_Pop10markers <- FindMarkers (disseq.integrated.test, ident.1 = "10", ident.2 = NULL, only.pos = TRUE, logfc.threshold = 3, mic.pct = 0.25)

head(brit_04res_12pop_Pop10markers)

##         p_val avg_logFC pct.1 pct.2 p_val_adj
## ZFYVE16     0  7.881338 0.860 0.224         0
## MRPS6       0  6.186249 0.922 0.281         0
## CA2         0  5.870670 0.879 0.080         0
## SERF2       0  5.169607 0.898 0.357         0
## TRPM3       0  5.034911 0.868 0.043         0
## TTR         0  4.171912 0.895 0.269         0

Pop10<-rownames(brit_04res_12pop_Pop10markers)


FeaturePlot(disseq.integrated.test, features = Pop10[1:12], max.cutoff = 3, cols = c("grey", "red"), pt.size = 0.1)

P10 <- WhichCells(disseq.integrated.test, idents = "10")
DimPlot(disseq.integrated.test, group.by =  'integrated_snn_res.0.4', pt.size = 0.1, label= TRUE, cells.highlight = (P10))

a<-VlnPlot(disseq.integrated.test, features = Pop10[1], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
b<-VlnPlot(disseq.integrated.test, features = Pop10[2], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
c<-VlnPlot(disseq.integrated.test, features = Pop10[3], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
d<-VlnPlot(disseq.integrated.test, features = Pop10[4], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
e<-VlnPlot(disseq.integrated.test, features = Pop10[5], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
f<-VlnPlot(disseq.integrated.test, features = Pop10[6], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

markers10<-plot_grid(a,b,c,d,e,f, nrow = 3)

#res4<-DimPlot(disseq.integrated.test, reduction = "umap", group.by = "integrated_snn_res.0.4", label = TRUE)  + NoLegend()

#Idents(disseq.integrated.test) <-"integrated_snn_res.0.4"

brit_04res_12pop_Pop11markers <- FindMarkers (disseq.integrated.test, ident.1 = "11", ident.2 = NULL, only.pos = TRUE, logfc.threshold = 3, mic.pct = 0.25)

head(brit_04res_12pop_Pop11markers)

##                 p_val avg_logFC pct.1 pct.2     p_val_adj
## C1orf61 1.793380e-273  3.853403 0.942 0.236 5.380141e-270
## SYNE2   5.441505e-190  3.391705 0.866 0.223 1.632451e-186
## PSAT1   8.396443e-152  4.315800 0.827 0.258 2.518933e-148
## GINS2   1.376120e-113  3.023520 0.754 0.194 4.128360e-110
## SOX2    2.900593e-113  4.661590 0.716 0.166 8.701778e-110
## HECW1    5.401378e-91  4.796709 0.608 0.087  1.620413e-87

Pop11<-rownames(brit_04res_12pop_Pop11markers)


FeaturePlot(disseq.integrated.test, features = Pop11[1:12], max.cutoff = 3, cols = c("grey", "red"), pt.size = 0.1)

P11 <- WhichCells(disseq.integrated.test, idents = "11")
DimPlot(disseq.integrated.test, group.by =  'integrated_snn_res.0.4', pt.size = 0.1, label= TRUE, cells.highlight = (P11))

a<-VlnPlot(disseq.integrated.test, features = Pop11[1], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
b<-VlnPlot(disseq.integrated.test, features = Pop11[2], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
c<-VlnPlot(disseq.integrated.test, features = Pop11[3], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
d<-VlnPlot(disseq.integrated.test, features = Pop11[4], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
e<-VlnPlot(disseq.integrated.test, features = Pop11[5], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)
f<-VlnPlot(disseq.integrated.test, features = Pop11[6], group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

markers11<-plot_grid(a,b,c,d,e,f, nrow = 3)

VlnPlot(disseq.integrated.test, features = "MKI67", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "DYNLRB2", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "PAX6", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "CALB1", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "FABP7", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "DCN", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "SOX2", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "DCX", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "TTR", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "SPARC", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "LHX2", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "PTN", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "VIM", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "NHLH1", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "CNTN2", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "NEUROD2", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "NEUROD6", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "DCC", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "NFIB", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "PAX3", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "TRIB3", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "TROAP", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "ASPM", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "DDIT3", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "NUPR1", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "ASPM", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

VlnPlot(disseq.integrated.test, features = "TROAP", group.by = "integrated_snn_res.0.4", pt.size = 0, combine = TRUE)

#VlnPlot(disseq.integrated.test, features = "ATR", group.by = "genotype", pt.size = 0, combine = TRUE)

#Look at NFL (Brad Margus)
#Preliminary focus on AHO173-MG samples and D60vs90 might work well
#Two Gimil/AT210c3 at different differentiation experiments might be nice to contrast intially
#query the 'pool' entry in metadata
#Need to assess whether to actually use htodemux or mutliseq. Multiseq might be higher quality but I have cells for some samples that are not present with htodemux.
#ROS genes, GSTT1, CAT
#Retrograde endocannabinoid transporter genes
#Are there african american A-T patients? Aboriginal australian?
#Can print out datatables in chunks using DF %>% DT()
#Devvo and Daveo meeeting 15/06 - Put code on Github, point to link with MG study samples on server and original object, re-evaluate UMAP PCs and nearest neighbours
#Subset doublets out